Recent News:

General News:

September 2011: Added a new web page that shows various forecasted quantities that
users and schedulers of the GBT might be interested in seeing. The
plotted quantities are determined from the forecasts that are described here and the algorithms that make up the GBT's Dynamical Scheduling System.

September 2011: Added Mustang-oriented overview plots and modified existing overview for the parameters of the new 68-92 GHz spectral-line receiver.

December, 2010: Pizza plots now use the updated algorithms in the latest versions of the DSS memos. Cloud coverage plots now reflect the algorithms in the draft memo on what constitutes good Mustang performance.

June 3, 2010: The function of the Cloud Coverage plot has been expanded
to include information concerning whether or not conditions are suitable for
Mustang observing. This is in addition to depicting the continuum
observing condition that I added earlier this year.

April 20, 2010: Added a new version of the overview (pizza) plot
that reflects the algorithms used by the "Dynamic
Scheduling System" for the GBT. All overview plots have been moved to their
own web pages in a hope of making these pages less formidable to its readers.

Feb. 25, 2010: Added plots for downward long-wave irradiance (LWd)
on the "Clouds and Precipitation" web pages. Added a color guideline to
those plot and to the cloud cover plot on this page that show when LWd
values are higher than the accepted Dynamical Scheduling System's
guidelines for continuum observing.

Nov 10, 2009: Two technical seminars from 2009 have been placed
on-line:

The palette of 7.5-day (GFS) plots has been reduced to only
time series. Elevation, height, and frequency plots for the current
time have been removed since the 3.5 day (NAM) forecasts contain better
results.

April 7, 2009: The Overview plot now uses wind values from the
"Point" forecasts for Green Bank for the first 6 days.

April 2, 2009: "Ground Weather Condition" and "Clouds and
Precipitation" forecasts now include "Point" forecast information for
Green Bank. This includes overlays of Green Bank data on the
traditional plots plus a few new forecasted quantities. See
Details below for more information.

Description of
Overview Plots:

Shows various forecasted quantities that
users and schedulers of the GBT might be interested in seeing. The
plotted quantities are determined from the forecasts that are described
at my "High Frequency Weather Forecasts"
pages and the algorithms that make up the GBT's Dynamic Scheduling
System

Overview that uses the full algorithms of the GBT's Dynamic Scheduling
System. This view is meant to depict which frequencies will be more or less
productive for the telescope, given the
competition that exists between different observing frequencies. As
such, it depicts the likelihood of what frequency projects the DSS will
choose for any time slot. Since the graph is a measure of
relative productiveness, the graph
does not indicate the absolute
productiveness of any particular time or frequency.

Shows the DSS determination of the
efficiency or productiveness of a particular frequency and time. It doesn't include any of the DSS's judgments as to whether the efficiency is too low for the telescope to be productive nor the DSS algorithms for the competitiveness between projects at different frequencies.

This plot uses a static value of REST's as a weather
criteria. This view is meant to depict whether or not
observations will be productive at a particular frequency where no judgment is made as to
whether one frequency will be more productive than another. This
plots works best for the older version of
dynamic scheduling when observers were given a choice between two or
more time slots and needed to pick which one will be more productive.

Details:

The forecasts presented on these pages are derived from three major
weather forecast products supplied by the national weather services.
(For more details, see: Weather
Forecasting For Radio Astronomy: Part I The Mechanisms and Physics).
The downloaded forecasts are either ground-level "Point" forecasts
(added on April 2, 2009) or the results of forecasts of vertical
weather profiles.

"Point" forecasts use sophisticated interpolation to provide details
for any location within the US at a resolution of 5 km, extend for 6
days with a time resolution of 1 hr, and are updated on the average of
nine times per day. These are supposedly the highest resolution
forecasts and, as such, should be the best predictors of the weather in
Green Bank. The 5 km cell I have chosen is centered at latitude=38.429,
longitude = -79.840, just south of the GBT. "Point" forecasts provide
only "ground-level" values, the quantities that humans are mostly
interested in (precipitation, temperature, etc.), but do not include
any information from which opacities can be determined. The
forecasts are downloaded and archived as often as the forecasts are
updated.

The majority of the results presented here are from 3.5 and 7.5 day
forecasts of vertical weather conditions. The forecasts are based on
computer models that use balloon soundings and GOES satellite soundings
as input. These very detailed forecasts are only available for a
limited number of locations. The three nearest to Green Bank are
Elkins, WV, Lewisburg, WV, and Hot Springs, VA, all about 45-60 miles
from the observatory. By 'averaging' the data from the three sites, it
is hoped that the results represent the conditions over the
observatory.

The 3.5 forecasts are based on the "North American Mesoscale" (NAM,
formally known as ETA) model. ".... The model is run four times a day
(00, 06, 12, 18 UTC) out to 84 hours. It is currently run with 12 km
horizontal resolution and with 1 hour temporal resolution, providing
finer detail than other operational forecast models."
(
https://en.wikipedia.org/wiki/North_American_Mesoscale_Model).

The 7.5-day forecasts are based on the the first half of the 16-day
GFS (Global Forecast System, previously AVN) atmospheric models. The
half of the model I use has a 35 km horizontal resolution, a 3 hour
temporal resolution, and is updated twice a day
(
https://en.wikipedia.org/wiki/Global_Forecast_System).
The courser resolution of the GFS model suggests that one should only
use the GFS results only beyond the 3.5 day cutoff of the NAM
forecasts.

Each vertical forecast consists of a time series of ground weather
conditions (pressure, temperature, wind speed and direction, dew
point...), gross weather conditions (cloud cover, total precipitable
water, ...) and, most importantly for the work presented here,
pressure, humidity, dew point, cloud fraction, ... as a function of
time and height above the above mentioned towns. The models divide the
atmosphere into ~64 layers that extend to over 20,000 m. Since these
forecasts and models are what the weather services use to predict
weather for our area, including the input to the "Point" forecasts,
it's one of the best data sets for local forecasting. The data are
downloaded from ftp://ftp.meteo.psu.edu
four times a day and archived for future use.

The calculations performed on these data, unlike most other attempts
at calculating weather conditions for cm- and mm-wave observations, are
not based on a model atmosphere with assumed pressure heights or
temperature lapse rates. Rather, calculations are performed for each
layer of the atmosphere using the forecasted conditions for that layer,
thereby eliminating any assumptions concerning the atmospheric profile
above the observatory. Calculations are performed every two hours in
TCL by a CLEO application
(http://www.gb.nrao.edu/~rmaddale/GBT/CLEOManual/index.html)
which also automatically generates these web pages.

The number of results that can be produced by the CLEO application
is rather extensive and only a subset of what is possible is presented
here. There's usually about thirty graphs that are given here
that I hope help our observers plan their observations and help with
their data calibration. The contents of these pages will change as we
learn more about what our observers and staff require.

As I've added various weather products to this work, I have also
maintained an archive of the NWS forecasts. In June 2005 I
released a CLEO graphical interface that allows users
the ability to generate graphs for just their desired weather
parameters from wither the current or archived data.

One can use the archive of forecasts either to determine weather
parameters for past observations or to accumulate weather
statistics. Because the forecasts are generated within a couple
of hours before the time for which they apply, the archive can be
considered a reasonably good representation of the actual weather
conditions. Furthermore, starting in Sept 2009, I have been
archiving "Rapid Update Cycle" (RUC) vertical forecasts.
According to
Wikipedia:
"The RUC runs at the highest frequency of any forecast model at the
National Centers
for Environmental Prediction
(NCEP), assimilating recent observations to provide very high frequency
updates of current conditions and short-range forecasts. This update
frequency is still only once an hour (the standard interval for
ASOS
observation reporting), and with current computational limitations and
the time required to assimilate all of the data, there is approximately
an hour delay in producing the forecasts. Because of this, it is common
practice to use a one-hour forecast from the RUC as a current analysis,
as the one-hour forecast comes out only a few minutes before the time
it is forecasting for. There is also little possibility for error in a
one-hour forecast, meaning that the RUC's one-hour forecast will not
usually vary greatly from the actual state of the atmosphere at that
particular point in time."

The following table shows the extent of the archives for the
various types of forecasts I have mentioned:

Forecast

NAM

GFS3

RUC

Point Map

Archive
start date

26 Apr 2004

17 Sept 2007

15 Sept 2009

13 Mar 2009

The following sections give a general guide as to what is usually
available as links off of this web page.

Ground
Weather Conditions

The displayed graphs consist of wind speeds (average ground level
winds, average at 75-m, and gust) and temperatures as a function of UT,
which should help observers avoid times when the weather conditions
either preclude observing (too cold or windy for the telescope to
operate) or wind conditions that are unsuitable to high frequency
observing. I include estimates of precipitable water vapor,
which can be used to predict roughly whether the opacity will be good
for a particular observing frequency. A better measure of opacity
is provided off of the "Opacity" or "Relative Effective System
Temperatures" links above.
also provide plots of dew point, humidity, and pressure. Some
quantities are provided by only the "Point" forecasts, some quantities
are provided only by the vertical profile forecasts, and some
quantities are provided by both. All "Point" forecast plots will have
"Green Bank" included in the legend to the plot.

Cloud
Cover and Precipitation

Added on Feb. 25, 2009. The displayed graphs consist of various
values as a function of UT. There are plots of fractional cloud cover
in the low, middle, and upper atmospheres, and a plot of the total
cloud cover. Usually lower-level and often middle-level cloud cover
will influence continuum mapping projects. Plots also show the
precipitation rates per hour in units of kg/m2. This is
essentially the same as rainfall in mm/hr. Since snow is less dense
than water, you can expect snow fall rates to be on the order of 10 mm
for each kg/m2. Finally, I provide graphs that indicate the
expected precipitation type, precipitation probabilities, the
likelihood of thunder, ceiling height, and visibility.
All "Point" forecast plots will have "Green Bank" included in the
legend to the plot.

Opacities

Opacities are derived via the MWP model of Liebe 1985), with some
modifications by Danese and Partridge (1989). Opacities are calculated
based on the contributions from 40 O2 resonance lines, three
H2O resonance lines, H2O continuum, and the dry
air. The model should be accurate for most purposes up to 120 GHz.

As of Sept, 2005, I have added in the contributions to opacity from
hydrosols (fog, cloud water droplets, etc.). I'm using the cloud model
described by Schwab and Hogg (1989), combined with the Liebe hydrosol
continuum model. It's a compromise technique and assumes a cloud is
present in any layer of the atmosphere where the humidity is 95% or
greater. The thickness of the cloud layer determines the density of
water droplets -- 0.2 g/m3 for clouds thinner than 120 m,
0.4 g/m3 for clouds thicker than 500 m, with
linearly-interpolated densities for clouds of intermediate thickness.

The opacity plots typically provided are:

an estimate of the current opacity as a function of frequency

and a time series of opacities for up to 7.5 days at selected
frequencies.

System
Temperatures

Once the opacity is calculated for each layer in the atmosphere, one
can then use radiative transfer to derive the contribution to the
system temperature that is due to the atmosphere. This quantity is
helpful since Tsys directly influences the noise in an observation. The
derived graphs show only that part of Tsys that is contributed by the
atmosphere. They do not include the contributions from the receiver,
spillover, and the 3K microwave background.

Plots consist of:

an estimate of the current atmospheric Tsys as a function of
frequency,

a predicted "tipping" curve of the current conditions for
selected frequencies

and a time series of Tsys values for up to 7.5 days at selected
frequencies.

Neither system temperatures or opacities alone determine
the affects of the atmosphere on observations. Firstly, the atmosphere
attenuates the atmospheric signal and, secondly, the atmosphere
emission can be a significant contributor to the system temperature.
Both the atmospheric attenuation and emission are important factors in
determining the amount of observing time needed to achieve a certain
signal to noise.

I define the Effective System Temperature (EST)
as Tsys*exp(Tau*AirMass). EST is proportional to the
square root of the integration time needed to achieve a desired signal
to noise. Tsys is the sum of the contributions from the
atmosphere at the observing elevation (Tatm*(1-exp(tau*AirMass))),
spillover (assumed to be 3 K for the GBT), the cosmic microwave
background (3 K), and the receiver. Thus, EST is receiver,
frequency, telescope, elevation, and weather dependent. (Note that I am
not including in EST the contribution of any strong continuum
source or background galactic emission.)

I next define the Relative Effective System
Temperature (REST) as EST / EST0 where EST0 is the
value of EST under the best possible weather conditions for
Green Bank for the same elevation at which EST is determined. REST
is exactly equal to sqrt(t/t0), where t is the integration time needed
to perform an observation under the current weather conditions and t0
is the integration time needed under the best possible weather
conditions. I used the weather conditions between 1 Oct 2004 and 1 May
2005 to determine values for EST0.

Plots on the forecast page consist of:

an estimate of the current REST as a
function of frequency,

an estimate of REST as a function of
elevation for the current conditions for selected frequencies

and a time series of REST values for up to
7.5 days at selected frequencies.

It might be useful to consider the following guiding
principles in using REST plots.

If REST = x, you will need to spend x^2 as much
telescope time to achieve the same signal to noise as you would if the
weather was as good as it can get.

When the REST for your observing frequency and
elevation is less than about sqrt(2), you probably should consider the
observing time as being productive.

If you are doing continuum observers or require
'photometric' conditions, you probably should observe only when the REST
is well below sqrt(2).

When REST is above 4, your observations
will probably be an unproductive use of the telescope's time.

Atmospheric
Temperatures

Classically, observers use a measure of Tsys as a
function of elevation (a 'tipping') to determine atmospheric opacity.
The usual problem is that one has to assume a representative
temperature (Tatm) for the atmosphere in this analysis. The degree to
which the assumed Tatm is wrong is directly reflected in the inaccuracy
of the derived opacity. These assumptions are no longer needed since
one can determine Tatm from vertical weather data.

Plots consist of:

an estimate of the current Tatm as a function of
frequency,

and a time series of Tatm values for up to 7.5
days at selected frequencies.

Refraction

Probably only staff will find the estimates of
refraction interesting. Like the other calculations presented here, one
can use vertical profiles and in-situ measures of the index of
refraction to derive the amount by which the telescope's pointing needs
to be adjusted for the difference between the refracted and true
elevation of a source.

I provide a comparison between the refraction derived
from vertical profiles to two other methods that are based on
ground-level weather data. The first is that produced by the SlaLib
refraction package which uses a standard lapse rate and pressure
heights, with ground level weather parameters (temperature, pressure,
and dew point). The other uses an empirical fit to the model presented
in GBT Memo 112 (Maddalena 1994) and the same ground weather values.
The latter model, except for the empirical fit, is that which is
currently in use by the GBT.

Additionally, the GBT has an interesting optics problem
due to refraction. Richard Simon (1994, private communication) first
pointed out that, since the top and bottom edge of the GBT are at two
very different elevation, the atmospheric paths for rays that hit the
top of the dish will pass through a different atmosphere than the rays
hitting the lower edge. This differential refraction will alter the
shape of objects that are observed close to the horizon, essentially
elongating sources in the elevation direction. The telescope will have
a virtual astigmatism that is due solely to the atmosphere. One of the
plots I present illustrates the magnitude of this differential
refraction on source size for each meter of aperture. The amount of
virtual astigmatism is very weather dependent and poses a challenge to
those wanting to observe close to the horizon. One way to correct for
this astigmatism is to properly deform the telescope's shape out of a
parabola in the elevation direction.

The refraction plots typically provided are:

a time series of refraction from vertical profiles (labeled SkewT)

the ground level derivative of this refraction (multiply by 100 meters
for the GBT). The amount one will need to deform the telescope's primary
is proportional to the displayed values.

differences between the SkewT, SlaLib, and GBT refraction models.

the current refraction as a function of height above the observatory

the derivative of the current refraction as a function of height above the observatory.